Operations leaders, founders, and business owners watching repetitive work eat their team's time and their margin. If people are spending hours on tasks a well-built system could handle — triage, summarising, answering the same questions, extracting from documents — there's usually real money on the table. This is about capturing it honestly, not chasing a headline number.
Where AI Cuts Cost
Two levers, both engineered honestly
Automate repetitive work
Route and triage requests, summarise and extract from documents, answer repeat questions, tag and categorise, and draft first-pass responses — freeing your team for work that actually needs a human.
Engineer AI to run cheap
The AI itself can become a cost if built carelessly. I engineer for spend from day one — caching repeated answers, right-sizing models, efficient retrieval — so per-request cost stays low.
Speed as savings
Faster turnaround is a cost saving too — less time per task, quicker delivery, fewer bottlenecks — often the easiest ROI to measure.
Know when NOT to
Truly bespoke, low-volume, or high-stakes work is often a poor fit — and I'll tell you when automation would cost more than it saves. Honest scoping is part of the value.
How an Engagement Works
1 · Assess
We map where your team spends repetitive effort and identify the highest-ROI candidates — with an honest estimate of the opportunity, not a sales number.
2 · Build
I build the automation with production discipline — evals, guardrails, and cost monitoring — so it's reliable and measurable, not a fragile demo.
3 · Measure & optimise
We measure the actual saving against a baseline and tune from there. If it doesn't pay off, you'll know — and why.
Cost engineering isn't a pitch for me; it's how I ship. On my own live AI product I run a semantic cache targeting roughly a 30% hit rate specifically to keep per-session AI cost under an economic ceiling, choose the smallest model that does each job, and monitor spend per request. Earlier, at CoinSwitch, I cut query latency 30% with Redis caching and a further 40% with connection pooling while holding the system stable from 600K to 10M users. The same discipline — measure, engineer, verify — is what I bring to reducing your costs.
Frequently Asked Questions
How can AI actually reduce my costs?
By automating repetitive high-volume work and by engineering the AI to run cheaply (caching, right-sized models, efficient retrieval). The savings are real when the work is genuinely repeatable and measured properly.
Can you guarantee a specific percentage?
No honest engineer promises a fixed number sight unseen. I assess your workflows, estimate realistically, build, and measure the actual result.
What work is a good fit?
High-volume, repetitive, rules-plus-judgment tasks: triage, summarising, document extraction, repeat questions, tagging, first-draft responses. Bespoke one-off work usually isn't.
How do you keep the AI itself from becoming a cost?
Engineering for spend from day one — semantic caching, smallest viable model, efficient retrieval, per-request monitoring — the same techniques I use on my own product.
How do we start?
Book a free call; we identify the highest-ROI automation candidates and you get an honest view of the opportunity.
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